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Creating the Perfect Product: The Role of Engineering Technologies

By: Brian Bell, Peter Spicka and Aniruddha Mukhopadhyay
Posted: October 13, 2008, from the April 2006 issue of GCI Magazine.

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Thus, given the needs for innovation and reducing time-to-market, advanced technology tools such as Computer Aided Engineering (CAE) have been relied on very heavily. CAE complements and often reduces the load on plant trials and experiments, particularly in terms of product design and process scale-up, trouble-shooting, retrofitting and optimization. Embedding CAE throughout the entire engineering and product development streams yields significant prospect for financial and competitive advantages in the marketplace.4–6

The current focus in the cosmetics and toiletries industry is on innovation. Innovation is not just R&D, and it does not happen overnight. As a business grows to maturity, organizations go through parallel maturity curves on consumer-space, internal-culture, technology and toolsets. Adoption of evolving technologies, along with reliably and cost-effectively embedding them in the innovation process, is key to optimizing time-to-market as well as improved product quality control. Early adopters have made significant progress in integrating modeling and simulation as a front-line tool to drive innovation in both their most recent distributed application R&D and their core platform technology areas.

Companies such as L’Oréal and P&G are the forerunners in this context.2,3 As more and more industry giants reorganize with simulation-enabled engineering, the marriage of simulation technologies with their traditional process technologies based on trials and experiments is creating a win-win situation. Nevertheless, it requires an icebreaker case study tailored to a specific corporate setup to illustrate the potential for net cost-saving as well as identify the prospects for strategic improvement in time-to-market.7,8 Such an illustration helps create the environment to influence the value chain in adjacent vertical product lines.

Acknowledgements

The authors would like to thank Armstrong Inc. and acknowledge contributions from their current and former colleagues at Fluent: Andre Bakker, Liz Marshall, Lanre Oshinowo, Ahmad Haidari, Eric Grald and Rafi Khan.